Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations19768
Missing cells10929
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 MiB
Average record size in memory375.9 B

Variable types

Categorical1
Boolean6
Text1
Numeric9
DateTime2

Alerts

followers is highly overall correlated with following and 6 other fieldsHigh correlation
following is highly overall correlated with followers and 4 other fieldsHigh correlation
label is highly overall correlated with text_bot_countHigh correlation
log_followers is highly overall correlated with followers and 6 other fieldsHigh correlation
log_following is highly overall correlated with followers and 4 other fieldsHigh correlation
log_public_gists is highly overall correlated with followers and 4 other fieldsHigh correlation
log_public_repos is highly overall correlated with followers and 6 other fieldsHigh correlation
public_gists is highly overall correlated with followers and 4 other fieldsHigh correlation
public_repos is highly overall correlated with followers and 6 other fieldsHigh correlation
text_bot_count is highly overall correlated with label and 1 other fieldsHigh correlation
type is highly overall correlated with text_bot_countHigh correlation
label is highly imbalanced (67.2%) Imbalance
type is highly imbalanced (92.8%) Imbalance
site_admin is highly imbalanced (95.8%) Imbalance
bio has 10929 (55.3%) missing values Missing
public_repos has 942 (4.8%) zeros Zeros
public_gists has 7961 (40.3%) zeros Zeros
followers has 1445 (7.3%) zeros Zeros
following has 6017 (30.4%) zeros Zeros
text_bot_count has 19003 (96.1%) zeros Zeros
log_public_repos has 942 (4.8%) zeros Zeros
log_public_gists has 7961 (40.3%) zeros Zeros
log_followers has 1445 (7.3%) zeros Zeros
log_following has 6017 (30.4%) zeros Zeros

Reproduction

Analysis started2024-11-21 09:11:21.395062
Analysis finished2024-11-21 09:11:29.907707
Duration8.51 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

label
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Human
18578 
Bot
 
1190

Length

Max length5
Median length5
Mean length4.8796034
Min length3

Characters and Unicode

Total characters96460
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHuman
2nd rowHuman
3rd rowHuman
4th rowBot
5th rowHuman

Common Values

ValueCountFrequency (%)
Human 18578
94.0%
Bot 1190
 
6.0%

Length

2024-11-21T17:11:29.986812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-21T17:11:30.063637image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
human 18578
94.0%
bot 1190
 
6.0%

Most occurring characters

ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76692
79.5%
Uppercase Letter 19768
 
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 18578
24.2%
m 18578
24.2%
a 18578
24.2%
n 18578
24.2%
o 1190
 
1.6%
t 1190
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
H 18578
94.0%
B 1190
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 18578
19.3%
u 18578
19.3%
m 18578
19.3%
a 18578
19.3%
n 18578
19.3%
B 1190
 
1.2%
o 1190
 
1.2%
t 1190
 
1.2%

type
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
19597 
False
 
171
ValueCountFrequency (%)
True 19597
99.1%
False 171
 
0.9%
2024-11-21T17:11:30.129983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

site_admin
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
19678 
True
 
90
ValueCountFrequency (%)
False 19678
99.5%
True 90
 
0.5%
2024-11-21T17:11:30.189600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

company
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
10794 
False
8974 
ValueCountFrequency (%)
True 10794
54.6%
False 8974
45.4%
2024-11-21T17:11:30.238107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

blog
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
11256 
True
8512 
ValueCountFrequency (%)
False 11256
56.9%
True 8512
43.1%
2024-11-21T17:11:30.306179image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

location
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
True
12691 
False
7077 
ValueCountFrequency (%)
True 12691
64.2%
False 7077
35.8%
2024-11-21T17:11:30.369956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

hireable
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.4 KiB
False
16470 
True
3298 
ValueCountFrequency (%)
False 16470
83.3%
True 3298
 
16.7%
2024-11-21T17:11:30.422802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

bio
Text

Missing 

Distinct8641
Distinct (%)97.8%
Missing10929
Missing (%)55.3%
Memory size1.6 MiB
2024-11-21T17:11:30.600160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length160
Median length116
Mean length61.460459
Min length1

Characters and Unicode

Total characters543249
Distinct characters1746
Distinct categories23 ?
Distinct scripts18 ?
Distinct blocks45 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8574 ?
Unique (%)97.0%

Sample

1st rowI just press the buttons randomly, and the program evolves...
2nd rowTime is unimportant, only life important.
3rd rowDone studying. Need challenges.
4th rowAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.
5th rowSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.
ValueCountFrequency (%)
3069
 
3.9%
and 2526
 
3.2%
engineer 1583
 
2.0%
software 1521
 
1.9%
of 1488
 
1.9%
at 1380
 
1.8%
developer 1236
 
1.6%
the 1086
 
1.4%
a 1038
 
1.3%
i 1033
 
1.3%
Other values (14754) 62407
79.6%
2024-11-21T17:11:30.938174image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70014
 
12.9%
e 49589
 
9.1%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.7%
r 31181
 
5.7%
i 28526
 
5.3%
s 19655
 
3.6%
l 14767
 
2.7%
Other values (1736) 203194
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 388595
71.5%
Space Separator 70192
 
12.9%
Uppercase Letter 43745
 
8.1%
Other Punctuation 23761
 
4.4%
Decimal Number 3557
 
0.7%
Control 2914
 
0.5%
Dash Punctuation 2560
 
0.5%
Other Letter 2016
 
0.4%
Other Symbol 2014
 
0.4%
Math Symbol 1750
 
0.3%
Other values (13) 2145
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
25
 
1.2%
20
 
1.0%
20
 
1.0%
14
 
0.7%
13
 
0.6%
13
 
0.6%
12
 
0.6%
11
 
0.5%
11
 
0.5%
11
 
0.5%
Other values (912) 1866
92.6%
Other Symbol
ValueCountFrequency (%)
141
 
7.0%
💻 86
 
4.3%
🍕 81
 
4.0%
71
 
3.5%
62
 
3.1%
👨 58
 
2.9%
58
 
2.9%
🚀 46
 
2.3%
🐁 39
 
1.9%
39
 
1.9%
Other values (429) 1333
66.2%
Lowercase Letter
ValueCountFrequency (%)
e 49589
12.8%
o 32360
 
8.3%
n 31402
 
8.1%
a 31366
 
8.1%
t 31195
 
8.0%
r 31181
 
8.0%
i 28526
 
7.3%
s 19655
 
5.1%
l 14767
 
3.8%
c 14228
 
3.7%
Other values (137) 104326
26.8%
Nonspacing Mark
ValueCountFrequency (%)
204
60.7%
̶ 10
 
3.0%
͡ 6
 
1.8%
̭ 6
 
1.8%
̯ 6
 
1.8%
͉ 6
 
1.8%
́ 5
 
1.5%
̘ 4
 
1.2%
̪ 4
 
1.2%
͜ 4
 
1.2%
Other values (45) 81
 
24.1%
Uppercase Letter
ValueCountFrequency (%)
S 5685
13.0%
C 3807
 
8.7%
E 3010
 
6.9%
I 2927
 
6.7%
P 2841
 
6.5%
D 2744
 
6.3%
A 2743
 
6.3%
M 2331
 
5.3%
T 2121
 
4.8%
F 1734
 
4.0%
Other values (34) 13802
31.6%
Other Punctuation
ValueCountFrequency (%)
. 7699
32.4%
, 5911
24.9%
@ 4168
17.5%
/ 2005
 
8.4%
: 865
 
3.6%
' 750
 
3.2%
& 663
 
2.8%
! 383
 
1.6%
# 310
 
1.3%
221
 
0.9%
Other values (24) 786
 
3.3%
Math Symbol
ValueCountFrequency (%)
| 1137
65.0%
+ 407
 
23.3%
> 70
 
4.0%
= 43
 
2.5%
< 39
 
2.2%
~ 26
 
1.5%
8
 
0.5%
4
 
0.2%
3
 
0.2%
2
 
0.1%
Other values (10) 11
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 659
18.5%
0 591
16.6%
1 581
16.3%
3 361
10.1%
9 268
7.5%
8 240
 
6.7%
6 234
 
6.6%
4 224
 
6.3%
5 216
 
6.1%
7 179
 
5.0%
Other values (3) 4
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 570
84.9%
] 58
 
8.6%
} 18
 
2.7%
9
 
1.3%
5
 
0.7%
4
 
0.6%
3
 
0.4%
2
 
0.3%
1
 
0.1%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 536
85.1%
[ 57
 
9.0%
{ 19
 
3.0%
7
 
1.1%
3
 
0.5%
2
 
0.3%
2
 
0.3%
2
 
0.3%
1
 
0.2%
1
 
0.2%
Modifier Symbol
ValueCountFrequency (%)
🏻 30
34.5%
¯ 16
18.4%
` 14
16.1%
🏽 10
 
11.5%
🏼 9
 
10.3%
^ 3
 
3.4%
🏾 3
 
3.4%
2
 
2.3%
Private Use
ValueCountFrequency (%)
6
40.0%
2
 
13.3%
2
 
13.3%
2
 
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Space Separator
ValueCountFrequency (%)
70014
99.7%
  61
 
0.1%
48
 
0.1%
  40
 
0.1%
27
 
< 0.1%
2
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
10
62.5%
ˌ 2
 
12.5%
ˈ 2
 
12.5%
1
 
6.2%
ː 1
 
6.2%
Other Number
ValueCountFrequency (%)
² 2
33.3%
1
16.7%
¹ 1
16.7%
1
16.7%
¼ 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 2511
98.1%
30
 
1.2%
18
 
0.7%
1
 
< 0.1%
Format
ValueCountFrequency (%)
142
96.6%
2
 
1.4%
­ 2
 
1.4%
1
 
0.7%
Final Punctuation
ValueCountFrequency (%)
29
60.4%
14
29.2%
» 5
 
10.4%
Currency Symbol
ValueCountFrequency (%)
$ 13
68.4%
5
 
26.3%
£ 1
 
5.3%
Initial Punctuation
ValueCountFrequency (%)
12
70.6%
4
 
23.5%
« 1
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_ 147
96.7%
5
 
3.3%
Control
ValueCountFrequency (%)
2914
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 432048
79.5%
Common 108419
 
20.0%
Han 1521
 
0.3%
Inherited 478
 
0.1%
Cyrillic 244
 
< 0.1%
Hangul 174
 
< 0.1%
Hiragana 155
 
< 0.1%
Katakana 79
 
< 0.1%
Arabic 67
 
< 0.1%
Greek 26
 
< 0.1%
Other values (8) 38
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
25
 
1.6%
20
 
1.3%
20
 
1.3%
14
 
0.9%
13
 
0.9%
13
 
0.9%
12
 
0.8%
11
 
0.7%
11
 
0.7%
11
 
0.7%
Other values (680) 1371
90.1%
Common
ValueCountFrequency (%)
70014
64.6%
. 7699
 
7.1%
, 5911
 
5.5%
@ 4168
 
3.8%
2914
 
2.7%
- 2511
 
2.3%
/ 2005
 
1.8%
| 1137
 
1.0%
: 865
 
0.8%
' 750
 
0.7%
Other values (574) 10445
 
9.6%
Latin
ValueCountFrequency (%)
e 49589
 
11.5%
o 32360
 
7.5%
n 31402
 
7.3%
a 31366
 
7.3%
t 31195
 
7.2%
r 31181
 
7.2%
i 28526
 
6.6%
s 19655
 
4.5%
l 14767
 
3.4%
c 14228
 
3.3%
Other values (107) 147779
34.2%
Hangul
ValueCountFrequency (%)
8
 
4.6%
7
 
4.0%
7
 
4.0%
5
 
2.9%
4
 
2.3%
4
 
2.3%
4
 
2.3%
4
 
2.3%
4
 
2.3%
3
 
1.7%
Other values (102) 124
71.3%
Inherited
ValueCountFrequency (%)
204
42.7%
142
29.7%
̶ 10
 
2.1%
͡ 6
 
1.3%
̭ 6
 
1.3%
̯ 6
 
1.3%
͉ 6
 
1.3%
́ 5
 
1.0%
̘ 4
 
0.8%
̪ 4
 
0.8%
Other values (46) 85
17.8%
Katakana
ValueCountFrequency (%)
10
 
12.7%
4
 
5.1%
4
 
5.1%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
3
 
3.8%
2
 
2.5%
Other values (32) 41
51.9%
Cyrillic
ValueCountFrequency (%)
а 27
 
11.1%
о 18
 
7.4%
т 18
 
7.4%
н 14
 
5.7%
е 13
 
5.3%
и 12
 
4.9%
в 12
 
4.9%
с 11
 
4.5%
у 9
 
3.7%
р 8
 
3.3%
Other values (31) 102
41.8%
Hiragana
ValueCountFrequency (%)
11
 
7.1%
11
 
7.1%
8
 
5.2%
7
 
4.5%
7
 
4.5%
7
 
4.5%
7
 
4.5%
6
 
3.9%
6
 
3.9%
6
 
3.9%
Other values (30) 79
51.0%
Arabic
ValueCountFrequency (%)
ا 10
14.9%
م 8
11.9%
و 7
10.4%
ت 6
 
9.0%
ل 5
 
7.5%
ر 4
 
6.0%
ع 4
 
6.0%
ة 3
 
4.5%
ي 3
 
4.5%
ى 2
 
3.0%
Other values (12) 15
22.4%
Greek
ValueCountFrequency (%)
ω 4
15.4%
λ 3
11.5%
π 2
 
7.7%
ς 2
 
7.7%
ρ 2
 
7.7%
θ 2
 
7.7%
ν 1
 
3.8%
Θ 1
 
3.8%
ο 1
 
3.8%
δ 1
 
3.8%
Other values (7) 7
26.9%
Hebrew
ValueCountFrequency (%)
מ 2
14.3%
ר 2
14.3%
ש 2
14.3%
ו 1
7.1%
א 1
7.1%
ל 1
7.1%
ע 1
7.1%
ה 1
7.1%
ח 1
7.1%
י 1
7.1%
Unknown
ValueCountFrequency (%)
6
40.0%
2
 
13.3%
2
 
13.3%
2
 
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Tibetan
ValueCountFrequency (%)
1
50.0%
1
50.0%
Thai
ValueCountFrequency (%)
2
100.0%
Kannada
ValueCountFrequency (%)
2
100.0%
Mandaic
ValueCountFrequency (%)
1
100.0%
Egyptian_Hieroglyphs
ValueCountFrequency (%)
𓀡 1
100.0%
Devanagari
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 537268
98.9%
None 1839
 
0.3%
CJK 1521
 
0.3%
Punctuation 576
 
0.1%
Block Elements 255
 
< 0.1%
Cyrillic 244
 
< 0.1%
VS 205
 
< 0.1%
Enclosed Alphanum Sup 181
 
< 0.1%
Hangul 165
 
< 0.1%
Dingbats 160
 
< 0.1%
Other values (35) 835
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70014
 
13.0%
e 49589
 
9.2%
o 32360
 
6.0%
n 31402
 
5.8%
a 31366
 
5.8%
t 31195
 
5.8%
r 31181
 
5.8%
i 28526
 
5.3%
s 19655
 
3.7%
l 14767
 
2.7%
Other values (86) 197213
36.7%
Punctuation
ValueCountFrequency (%)
221
38.4%
142
24.7%
48
 
8.3%
30
 
5.2%
29
 
5.0%
27
 
4.7%
18
 
3.1%
14
 
2.4%
12
 
2.1%
12
 
2.1%
Other values (10) 23
 
4.0%
VS
ValueCountFrequency (%)
204
99.5%
1
 
0.5%
Block Elements
ValueCountFrequency (%)
141
55.3%
62
24.3%
39
 
15.3%
11
 
4.3%
1
 
0.4%
1
 
0.4%
None
ValueCountFrequency (%)
💻 86
 
4.7%
🍕 81
 
4.4%
73
 
4.0%
  61
 
3.3%
👨 58
 
3.2%
55
 
3.0%
· 54
 
2.9%
🚀 46
 
2.5%
  40
 
2.2%
🐁 39
 
2.1%
Other values (407) 1246
67.8%
Dingbats
ValueCountFrequency (%)
71
44.4%
58
36.2%
5
 
3.1%
5
 
3.1%
3
 
1.9%
2
 
1.2%
2
 
1.2%
2
 
1.2%
1
 
0.6%
1
 
0.6%
Other values (10) 10
 
6.2%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🇦 31
17.1%
🇺 29
16.0%
🇧 17
9.4%
🇷 15
8.3%
🇨 15
8.3%
🇬 11
 
6.1%
🇪 8
 
4.4%
🇸 8
 
4.4%
🇾 6
 
3.3%
🇮 6
 
3.3%
Other values (13) 35
19.3%
Misc Symbols
ValueCountFrequency (%)
30
21.9%
21
15.3%
14
10.2%
12
 
8.8%
9
 
6.6%
5
 
3.6%
4
 
2.9%
4
 
2.9%
3
 
2.2%
3
 
2.2%
Other values (24) 32
23.4%
Cyrillic
ValueCountFrequency (%)
а 27
 
11.1%
о 18
 
7.4%
т 18
 
7.4%
н 14
 
5.7%
е 13
 
5.3%
и 12
 
4.9%
в 12
 
4.9%
с 11
 
4.5%
у 9
 
3.7%
р 8
 
3.3%
Other values (31) 102
41.8%
CJK
ValueCountFrequency (%)
25
 
1.6%
20
 
1.3%
20
 
1.3%
14
 
0.9%
13
 
0.9%
13
 
0.9%
12
 
0.8%
11
 
0.7%
11
 
0.7%
11
 
0.7%
Other values (680) 1371
90.1%
Hiragana
ValueCountFrequency (%)
11
 
7.1%
11
 
7.1%
8
 
5.2%
7
 
4.5%
7
 
4.5%
7
 
4.5%
7
 
4.5%
6
 
3.9%
6
 
3.9%
6
 
3.9%
Other values (30) 79
51.0%
Katakana
ValueCountFrequency (%)
10
 
11.9%
10
 
11.9%
5
 
6.0%
4
 
4.8%
4
 
4.8%
3
 
3.6%
3
 
3.6%
3
 
3.6%
3
 
3.6%
3
 
3.6%
Other values (27) 36
42.9%
Arabic
ValueCountFrequency (%)
ا 10
14.7%
م 8
11.8%
و 7
10.3%
ت 6
 
8.8%
ل 5
 
7.4%
ر 4
 
5.9%
ع 4
 
5.9%
ة 3
 
4.4%
ي 3
 
4.4%
ى 2
 
2.9%
Other values (13) 16
23.5%
Diacriticals
ValueCountFrequency (%)
̶ 10
 
7.8%
͡ 6
 
4.7%
̭ 6
 
4.7%
̯ 6
 
4.7%
͉ 6
 
4.7%
́ 5
 
3.9%
̘ 4
 
3.1%
̪ 4
 
3.1%
͜ 4
 
3.1%
̩ 4
 
3.1%
Other values (40) 73
57.0%
Compat Jamo
ValueCountFrequency (%)
8
100.0%
Geometric Shapes Ext
ValueCountFrequency (%)
🟦 8
57.1%
🟨 6
42.9%
Arrows
ValueCountFrequency (%)
8
53.3%
4
26.7%
3
 
20.0%
Emoticons
ValueCountFrequency (%)
🙈 7
 
11.9%
🙉 6
 
10.2%
😄 4
 
6.8%
🙂 4
 
6.8%
😎 3
 
5.1%
😉 2
 
3.4%
🙏 2
 
3.4%
😍 2
 
3.4%
😁 2
 
3.4%
😋 2
 
3.4%
Other values (18) 25
42.4%
Hangul
ValueCountFrequency (%)
7
 
4.2%
7
 
4.2%
5
 
3.0%
4
 
2.4%
4
 
2.4%
4
 
2.4%
4
 
2.4%
4
 
2.4%
3
 
1.8%
3
 
1.8%
Other values (100) 120
72.7%
Geometric Shapes
ValueCountFrequency (%)
6
31.6%
2
 
10.5%
2
 
10.5%
2
 
10.5%
2
 
10.5%
2
 
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Box Drawing
ValueCountFrequency (%)
6
28.6%
6
28.6%
5
23.8%
3
14.3%
1
 
4.8%
PUA
ValueCountFrequency (%)
6
40.0%
2
 
13.3%
2
 
13.3%
2
 
13.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Letterlike Symbols
ValueCountFrequency (%)
5
100.0%
Currency Symbols
ValueCountFrequency (%)
5
100.0%
Sup Punctuation
ValueCountFrequency (%)
4
100.0%
Math Operators
ValueCountFrequency (%)
3
23.1%
2
15.4%
2
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Hebrew
ValueCountFrequency (%)
מ 2
14.3%
ר 2
14.3%
ש 2
14.3%
ו 1
7.1%
א 1
7.1%
ל 1
7.1%
ע 1
7.1%
ה 1
7.1%
ח 1
7.1%
י 1
7.1%
Misc Technical
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
IPA Ext
ValueCountFrequency (%)
ʖ 2
20.0%
ʕ 1
10.0%
ʔ 1
10.0%
ʀ 1
10.0%
ɴ 1
10.0%
ɾ 1
10.0%
ɚ 1
10.0%
ɹ 1
10.0%
ɛ 1
10.0%
Math Alphanum
ValueCountFrequency (%)
𝘵 2
 
8.3%
𝘴 2
 
8.3%
𝘭 2
 
8.3%
𝘶 2
 
8.3%
𝒾 1
 
4.2%
𝒽 1
 
4.2%
𝐂 1
 
4.2%
𝐑 1
 
4.2%
𝟎 1
 
4.2%
𝟖 1
 
4.2%
Other values (10) 10
41.7%
Phonetic Ext
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Thai
ValueCountFrequency (%)
2
100.0%
CJK Compat Forms
ValueCountFrequency (%)
2
100.0%
Modifier Letters
ValueCountFrequency (%)
ˌ 2
40.0%
ˈ 2
40.0%
ː 1
20.0%
Kannada
ValueCountFrequency (%)
2
100.0%
Diacriticals Sup
ValueCountFrequency (%)
1
50.0%
1
50.0%
Mandaic
ValueCountFrequency (%)
1
100.0%
Mahjong
ValueCountFrequency (%)
🀄 1
100.0%
Jamo
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Latin Ext Additional
ValueCountFrequency (%)
1
50.0%
1
50.0%
Egyptian Hieroglyphs
ValueCountFrequency (%)
𓀡 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
1
100.0%
Tibetan
ValueCountFrequency (%)
1
50.0%
1
50.0%
Devanagari
ValueCountFrequency (%)
1
100.0%

public_repos
Real number (ℝ)

High correlation  Zeros 

Distinct674
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3934449
Minimum0
Maximum10.819798
Zeros942
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:31.090177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.69314718
Q12.4849066
median3.5835189
Q34.4308168
95-th percentile5.5254529
Maximum10.819798
Range10.819798
Interquartile range (IQR)1.9459101

Descriptive statistics

Standard deviation1.4801216
Coefficient of variation (CV)0.4361708
Kurtosis0.063401207
Mean3.3934449
Median Absolute Deviation (MAD)0.94446161
Skewness-0.38244821
Sum67081.618
Variance2.1907598
MonotonicityNot monotonic
2024-11-21T17:11:31.199189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942
 
4.8%
0.6931471806 551
 
2.8%
1.098612289 465
 
2.4%
1.386294361 396
 
2.0%
1.609437912 380
 
1.9%
1.945910149 364
 
1.8%
1.791759469 357
 
1.8%
2.079441542 330
 
1.7%
2.302585093 312
 
1.6%
2.197224577 307
 
1.6%
Other values (664) 15364
77.7%
ValueCountFrequency (%)
0 942
4.8%
0.6931471806 551
2.8%
1.098612289 465
2.4%
1.386294361 396
2.0%
1.609437912 380
1.9%
1.791759469 357
 
1.8%
1.945910149 364
 
1.8%
2.079441542 330
 
1.7%
2.197224577 307
 
1.6%
2.302585093 312
 
1.6%
ValueCountFrequency (%)
10.81979828 1
< 0.1%
10.23088301 1
< 0.1%
10.17964092 1
< 0.1%
10.02654554 1
< 0.1%
9.937599082 1
< 0.1%
9.765718623 1
< 0.1%
9.740144754 1
< 0.1%
9.731512288 1
< 0.1%
9.176473302 1
< 0.1%
9.164819857 1
< 0.1%

public_gists
Real number (ℝ)

High correlation  Zeros 

Distinct359
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3667909
Minimum0
Maximum10.929207
Zeros7961
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:31.297961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.0986123
Q32.3978953
95-th percentile4.2046926
Maximum10.929207
Range10.929207
Interquartile range (IQR)2.3978953

Descriptive statistics

Standard deviation1.4937885
Coefficient of variation (CV)1.0929166
Kurtosis0.26107473
Mean1.3667909
Median Absolute Deviation (MAD)1.0986123
Skewness0.93069164
Sum27018.723
Variance2.231404
MonotonicityNot monotonic
2024-11-21T17:11:31.390853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7961
40.3%
0.6931471806 1873
 
9.5%
1.098612289 1152
 
5.8%
1.386294361 823
 
4.2%
1.609437912 665
 
3.4%
1.791759469 627
 
3.2%
1.945910149 488
 
2.5%
2.079441542 405
 
2.0%
2.302585093 327
 
1.7%
2.197224577 318
 
1.6%
Other values (349) 5129
25.9%
ValueCountFrequency (%)
0 7961
40.3%
0.6931471806 1873
 
9.5%
1.098612289 1152
 
5.8%
1.386294361 823
 
4.2%
1.609437912 665
 
3.4%
1.791759469 627
 
3.2%
1.945910149 488
 
2.5%
2.079441542 405
 
2.0%
2.197224577 318
 
1.6%
2.302585093 327
 
1.7%
ValueCountFrequency (%)
10.92920652 1
< 0.1%
10.89044176 1
< 0.1%
10.27311821 1
< 0.1%
10.19913779 1
< 0.1%
9.647497927 1
< 0.1%
9.269080867 1
< 0.1%
8.146419323 1
< 0.1%
8.061802275 1
< 0.1%
7.850103545 1
< 0.1%
7.467942332 1
< 0.1%

followers
Real number (ℝ)

High correlation  Zeros 

Distinct1598
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5025164
Minimum0
Maximum11.469527
Zeros1445
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:31.481852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.0794415
median3.5263605
Q34.8362819
95-th percentile6.7298241
Maximum11.469527
Range11.469527
Interquartile range (IQR)2.7568404

Descriptive statistics

Standard deviation1.9557633
Coefficient of variation (CV)0.55838804
Kurtosis-0.29389155
Mean3.5025164
Median Absolute Deviation (MAD)1.3291359
Skewness0.12973968
Sum69237.744
Variance3.8250099
MonotonicityNot monotonic
2024-11-21T17:11:31.578852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1445
 
7.3%
0.6931471806 803
 
4.1%
1.098612289 623
 
3.2%
1.386294361 515
 
2.6%
1.609437912 450
 
2.3%
1.791759469 415
 
2.1%
1.945910149 396
 
2.0%
2.079441542 347
 
1.8%
2.197224577 338
 
1.7%
2.302585093 311
 
1.6%
Other values (1588) 14125
71.5%
ValueCountFrequency (%)
0 1445
7.3%
0.6931471806 803
4.1%
1.098612289 623
3.2%
1.386294361 515
 
2.6%
1.609437912 450
 
2.3%
1.791759469 415
 
2.1%
1.945910149 396
 
2.0%
2.079441542 347
 
1.8%
2.197224577 338
 
1.7%
2.302585093 311
 
1.6%
ValueCountFrequency (%)
11.46952724 1
< 0.1%
11.35017121 1
< 0.1%
11.10049616 1
< 0.1%
10.97597829 1
< 0.1%
10.34563811 1
< 0.1%
10.31850687 1
< 0.1%
10.2995755 1
< 0.1%
10.28926003 1
< 0.1%
10.25456687 1
< 0.1%
10.15874973 1
< 0.1%

following
Real number (ℝ)

High correlation  Zeros 

Distinct620
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8589591
Minimum0
Maximum10.231928
Zeros6017
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:31.675870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.6094379
Q33.1354942
95-th percentile5.0039463
Maximum10.231928
Range10.231928
Interquartile range (IQR)3.1354942

Descriptive statistics

Standard deviation1.743082
Coefficient of variation (CV)0.93766562
Kurtosis-0.25441172
Mean1.8589591
Median Absolute Deviation (MAD)1.6094379
Skewness0.68128993
Sum36747.903
Variance3.0383349
MonotonicityNot monotonic
2024-11-21T17:11:31.769868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6017
30.4%
0.6931471806 1734
 
8.8%
1.098612289 1092
 
5.5%
1.386294361 794
 
4.0%
1.609437912 602
 
3.0%
1.791759469 533
 
2.7%
1.945910149 484
 
2.4%
2.079441542 407
 
2.1%
2.197224577 368
 
1.9%
2.302585093 322
 
1.6%
Other values (610) 7415
37.5%
ValueCountFrequency (%)
0 6017
30.4%
0.6931471806 1734
 
8.8%
1.098612289 1092
 
5.5%
1.386294361 794
 
4.0%
1.609437912 602
 
3.0%
1.791759469 533
 
2.7%
1.945910149 484
 
2.4%
2.079441542 407
 
2.1%
2.197224577 368
 
1.9%
2.302585093 322
 
1.6%
ValueCountFrequency (%)
10.23192762 1
< 0.1%
9.725675811 1
< 0.1%
9.676084944 1
< 0.1%
9.386140712 1
< 0.1%
9.236884927 1
< 0.1%
9.182043773 1
< 0.1%
9.178540059 1
< 0.1%
9.162514742 1
< 0.1%
9.145054905 1
< 0.1%
8.905851181 1
< 0.1%
Distinct19767
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size154.6 KiB
Minimum2008-01-27 07:09:47
Maximum2021-12-20 05:29:41
2024-11-21T17:11:31.857017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:31.955913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct19633
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size154.6 KiB
Minimum2016-08-08 22:18:09
Maximum2023-10-14 14:33:48
2024-11-21T17:11:32.041878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:32.155863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

text_bot_count
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.061361797
Minimum0
Maximum5
Zeros19003
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:32.233994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.34100309
Coefficient of variation (CV)5.5572539
Kurtosis51.672415
Mean0.061361797
Median Absolute Deviation (MAD)0
Skewness6.674794
Sum1213
Variance0.11628311
MonotonicityNot monotonic
2024-11-21T17:11:32.305832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19003
96.1%
1 425
 
2.1%
2 251
 
1.3%
3 75
 
0.4%
4 9
 
< 0.1%
5 5
 
< 0.1%
ValueCountFrequency (%)
0 19003
96.1%
1 425
 
2.1%
2 251
 
1.3%
3 75
 
0.4%
4 9
 
< 0.1%
5 5
 
< 0.1%
ValueCountFrequency (%)
5 5
 
< 0.1%
4 9
 
< 0.1%
3 75
 
0.4%
2 251
 
1.3%
1 425
 
2.1%
0 19003
96.1%

log_public_repos
Real number (ℝ)

High correlation  Zeros 

Distinct674
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4003517
Minimum0
Maximum2.4697759
Zeros942
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:32.389746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52658903
Q11.2484413
median1.522467
Q31.6920895
95-th percentile1.8757104
Maximum2.4697759
Range2.4697759
Interquartile range (IQR)0.44364829

Descriptive statistics

Standard deviation0.44557258
Coefficient of variation (CV)0.3181862
Kurtosis2.5132055
Mean1.4003517
Median Absolute Deviation (MAD)0.19942773
Skewness-1.592366
Sum27682.152
Variance0.19853492
MonotonicityNot monotonic
2024-11-21T17:11:32.499056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 942
 
4.8%
0.5265890341 551
 
2.8%
0.7412763114 465
 
2.4%
0.8697416862 396
 
2.0%
0.9591348389 380
 
1.9%
1.080417818 364
 
1.8%
1.026672031 357
 
1.8%
1.124748263 330
 
1.7%
1.194705523 312
 
1.6%
1.162283114 307
 
1.6%
Other values (664) 15364
77.7%
ValueCountFrequency (%)
0 942
4.8%
0.5265890341 551
2.8%
0.7412763114 465
2.4%
0.8697416862 396
2.0%
0.9591348389 380
1.9%
1.026672031 357
 
1.8%
1.080417818 364
 
1.8%
1.124748263 330
 
1.7%
1.162283114 307
 
1.6%
1.194705523 312
 
1.6%
ValueCountFrequency (%)
2.469775946 1
< 0.1%
2.418667395 1
< 0.1%
2.41409435 1
< 0.1%
2.400305597 1
< 0.1%
2.392206311 1
< 0.1%
2.376366884 1
< 0.1%
2.373988567 1
< 0.1%
2.373184487 1
< 0.1%
2.320078517 1
< 0.1%
2.318932725 1
< 0.1%

log_public_gists
Real number (ℝ)

High correlation  Zeros 

Distinct359
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66694448
Minimum0
Maximum2.4789897
Zeros7961
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:32.602469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.74127631
Q31.2231562
95-th percentile1.6495606
Maximum2.4789897
Range2.4789897
Interquartile range (IQR)1.2231562

Descriptive statistics

Standard deviation0.6235032
Coefficient of variation (CV)0.93486522
Kurtosis-1.4067386
Mean0.66694448
Median Absolute Deviation (MAD)0.74127631
Skewness0.22820652
Sum13184.158
Variance0.38875624
MonotonicityNot monotonic
2024-11-21T17:11:32.698587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7961
40.3%
0.5265890341 1873
 
9.5%
0.7412763114 1152
 
5.8%
0.8697416862 823
 
4.2%
0.9591348389 665
 
3.4%
1.026672031 627
 
3.2%
1.080417818 488
 
2.5%
1.124748263 405
 
2.0%
1.194705523 327
 
1.7%
1.162283114 318
 
1.6%
Other values (349) 5129
25.9%
ValueCountFrequency (%)
0 7961
40.3%
0.5265890341 1873
 
9.5%
0.7412763114 1152
 
5.8%
0.8697416862 823
 
4.2%
0.9591348389 665
 
3.4%
1.026672031 627
 
3.2%
1.080417818 488
 
2.5%
1.124748263 405
 
2.0%
1.162283114 318
 
1.6%
1.194705523 327
 
1.7%
ValueCountFrequency (%)
2.478989722 1
< 0.1%
2.475734864 1
< 0.1%
2.422420972 1
< 0.1%
2.415836793 1
< 0.1%
2.365324928 1
< 0.1%
2.329137523 1
< 0.1%
2.213362472 1
< 0.1%
2.204068027 1
< 0.1%
2.180429159 1
< 0.1%
2.136287543 1
< 0.1%

log_followers
Real number (ℝ)

High correlation  Zeros 

Distinct1598
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3804014
Minimum0
Maximum2.5232878
Zeros1445
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:32.800326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1247483
median1.5099182
Q31.7640939
95-th percentile2.0450861
Maximum2.5232878
Range2.5232878
Interquartile range (IQR)0.63934567

Descriptive statistics

Standard deviation0.54844034
Coefficient of variation (CV)0.39730496
Kurtosis0.65588584
Mean1.3804014
Median Absolute Deviation (MAD)0.28905272
Skewness-1.0720772
Sum27287.775
Variance0.30078681
MonotonicityNot monotonic
2024-11-21T17:11:32.907327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1445
 
7.3%
0.5265890341 803
 
4.1%
0.7412763114 623
 
3.2%
0.8697416862 515
 
2.6%
0.9591348389 450
 
2.3%
1.026672031 415
 
2.1%
1.080417818 396
 
2.0%
1.124748263 347
 
1.8%
1.162283114 338
 
1.7%
1.194705523 311
 
1.6%
Other values (1588) 14125
71.5%
ValueCountFrequency (%)
0 1445
7.3%
0.5265890341 803
4.1%
0.7412763114 623
3.2%
0.8697416862 515
 
2.6%
0.9591348389 450
 
2.3%
1.026672031 415
 
2.1%
1.080417818 396
 
2.0%
1.124748263 347
 
1.8%
1.162283114 338
 
1.7%
1.194705523 311
 
1.6%
ValueCountFrequency (%)
2.523287847 1
< 0.1%
2.513669926 1
< 0.1%
2.493246457 1
< 0.1%
2.482902834 1
< 0.1%
2.428833363 1
< 0.1%
2.426439162 1
< 0.1%
2.424765159 1
< 0.1%
2.423851834 1
< 0.1%
2.42077399 1
< 0.1%
2.41222392 1
< 0.1%

log_following
Real number (ℝ)

High correlation  Zeros 

Distinct620
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84720374
Minimum0
Maximum2.4187604
Zeros6017
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size154.6 KiB
2024-11-21T17:11:33.007327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.95913484
Q31.4196068
95-th percentile1.792417
Maximum2.4187604
Range2.4187604
Interquartile range (IQR)1.4196068

Descriptive statistics

Standard deviation0.65798899
Coefficient of variation (CV)0.77665968
Kurtosis-1.392483
Mean0.84720374
Median Absolute Deviation (MAD)0.54416616
Skewness-0.084293073
Sum16747.524
Variance0.4329495
MonotonicityNot monotonic
2024-11-21T17:11:33.106326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6017
30.4%
0.5265890341 1734
 
8.8%
0.7412763114 1092
 
5.5%
0.8697416862 794
 
4.0%
0.9591348389 602
 
3.0%
1.026672031 533
 
2.7%
1.080417818 484
 
2.4%
1.124748263 407
 
2.1%
1.162283114 368
 
1.9%
1.194705523 322
 
1.6%
Other values (610) 7415
37.5%
ValueCountFrequency (%)
0 6017
30.4%
0.5265890341 1734
 
8.8%
0.7412763114 1092
 
5.5%
0.8697416862 794
 
4.0%
0.9591348389 602
 
3.0%
1.026672031 533
 
2.7%
1.080417818 484
 
2.4%
1.124748263 407
 
2.1%
1.162283114 368
 
1.9%
1.194705523 322
 
1.6%
ValueCountFrequency (%)
2.418760403 1
< 0.1%
2.372640476 1
< 0.1%
2.368006188 1
< 0.1%
2.340472294 1
< 0.1%
2.325997367 1
< 0.1%
2.320625755 1
< 0.1%
2.320281588 1
< 0.1%
2.318705926 1
< 0.1%
2.316986385 1
< 0.1%
2.293125611 1
< 0.1%

Interactions

2024-11-21T17:11:28.879193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.003666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.645902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.312896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.993787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.643607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:26.899081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.594146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.240288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.958747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.072668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.725538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.391195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.075787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.726184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:26.965881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.669147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.313098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:29.034437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.137474image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.800020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.467177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.150787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.796421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.036881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.742147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.385098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:29.106443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.209592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.892079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.537191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.220787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.879772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.134897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.816289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.454098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:29.176444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.264195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.963741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.606177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.276319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.993294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.215882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.883288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.521099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:29.246443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.345008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.027376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.676177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.349393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:26.617957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.289463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.953287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.588100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:29.318003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.426964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.093857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.749177image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.416411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:26.684167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.365956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.037288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.659476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:29.393003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.514739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.177034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.826178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.479673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:26.756066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.440954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.104303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.727491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:29.464004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:23.584023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.245325image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:24.903786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:25.565853image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:26.829082image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:27.519146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.171288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-21T17:11:28.798440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-21T17:11:33.180326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
blogcompanyfollowersfollowinghireablelabellocationlog_followerslog_followinglog_public_gistslog_public_repospublic_gistspublic_repossite_admintext_bot_counttype
blog1.0000.2580.4270.3590.2180.0240.3690.4290.3610.3680.3670.3570.3650.0050.0620.080
company0.2581.0000.2590.1960.0570.0700.3920.2650.2040.1910.2010.1800.1980.0250.0690.102
followers0.4270.2591.0000.5370.2140.1630.3981.0000.5370.5970.6510.5970.6510.079-0.1500.226
following0.3590.1960.5371.0000.2700.1650.3590.5371.0000.4380.5370.4380.5370.000-0.1590.114
hireable0.2180.0570.2140.2701.0000.0580.1780.2130.2720.2050.2330.1990.2320.0130.0490.040
label0.0240.0700.1630.1650.0581.0000.1300.1910.1990.1550.4150.1410.3690.0060.5790.368
location0.3690.3920.3980.3590.1780.1301.0000.4000.3690.3090.3610.2880.3570.0190.1310.124
log_followers0.4290.2651.0000.5370.2130.1910.4001.0000.5370.5970.6510.5970.6510.077-0.1500.332
log_following0.3610.2040.5371.0000.2720.1990.3690.5371.0000.4380.5370.4380.5370.010-0.1590.140
log_public_gists0.3680.1910.5970.4380.2050.1550.3090.5970.4381.0000.6361.0000.6360.023-0.1370.112
log_public_repos0.3670.2010.6510.5370.2330.4150.3610.6510.5370.6361.0000.6361.0000.024-0.2040.417
public_gists0.3570.1800.5970.4380.1990.1410.2880.5970.4381.0000.6361.0000.6360.026-0.1370.091
public_repos0.3650.1980.6510.5370.2320.3690.3570.6510.5370.6361.0000.6361.0000.022-0.2040.326
site_admin0.0050.0250.0790.0000.0130.0060.0190.0770.0100.0230.0240.0260.0221.0000.0000.000
text_bot_count0.0620.069-0.150-0.1590.0490.5790.131-0.150-0.159-0.137-0.204-0.137-0.2040.0001.0000.510
type0.0800.1020.2260.1140.0400.3680.1240.3320.1400.1120.4170.0910.3260.0000.5101.000

Missing values

2024-11-21T17:11:29.591271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-21T17:11:29.816025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
0HumanTrueFalseFalseFalseFalseFalseNaN3.2958370.6931471.7917590.6931472011-09-26 17:27:032023-10-13 11:21:1001.4576460.5265891.0266720.526589
1HumanTrueFalseFalseTrueFalseTrueI just press the buttons randomly, and the program evolves...3.4339871.3862942.3025851.9459102015-06-29 10:12:462023-10-07 06:26:1401.4892990.8697421.1947061.080418
2HumanTrueFalseTrueTrueTrueTrueTime is unimportant,\nonly life important.4.6443913.9120237.1008525.4026772008-08-29 16:20:032023-10-02 02:11:2101.7306621.5916862.0919691.856716
3BotTrueFalseFalseFalseTrueFalseNaN3.9120230.0000004.4426511.0986122014-05-20 18:43:092023-10-12 12:54:5901.5916860.0000001.6942660.741276
4HumanTrueFalseFalseFalseFalseTrueNaN2.4849070.6931471.9459101.0986122012-08-16 14:19:132023-10-06 11:58:4101.2484410.5265891.0804180.741276
5HumanTrueFalseTrueTrueTrueFalseDone studying. Need challenges.4.0430510.6931473.1354942.0794422017-04-11 14:08:072023-10-11 05:59:2601.6180110.5265891.4196071.124748
6HumanTrueFalseTrueTrueTrueTrueAdministrator of MOONGIFT that is introducing open source software everyday to Japanese engineers since 2004.5.6276217.0387844.1588832.8332132008-04-07 22:22:222023-09-27 09:04:5601.8912462.0842781.6407201.343703
7HumanTrueFalseTrueFalseTrueFalseSenior Software Engineer at Google, working on Certificate Transparency and generalized transparency.3.6375860.6931473.1354940.0000002012-01-19 21:57:072023-08-07 16:06:3401.5341940.5265891.4196070.000000
8HumanTrueFalseFalseFalseFalseFalseNaN3.3322051.0986123.6375866.3919172019-12-24 20:04:332023-10-12 11:55:0101.4660770.7412761.5341942.000387
9HumanTrueFalseTrueTrueTrueFalseHi3.7612002.3025852.7080501.0986122013-07-23 23:29:342023-10-09 20:47:0501.5605001.1947061.3105060.741276
labeltypesite_admincompanybloglocationhireablebiopublic_repospublic_gistsfollowersfollowingcreated_atupdated_attext_bot_countlog_public_reposlog_public_gistslog_followerslog_following
19758HumanTrueFalseTrueFalseTrueFalseNaN3.4339870.0000002.3978952.4849072016-09-10 09:45:002023-10-06 11:30:5101.4892990.0000001.2231561.248441
19759HumanTrueFalseFalseFalseTrueTrueNaN3.6375862.9957324.5217891.9459102012-04-19 03:27:142023-10-07 18:13:5201.5341941.3852271.7087021.080418
19760BotTrueFalseFalseFalseFalseFalseI am the bot account of @alvaroaleman0.6931470.0000000.0000000.0000002018-12-15 19:55:312021-07-27 14:14:2520.5265890.0000000.0000000.000000
19761HumanTrueFalseFalseFalseFalseFalseNaN1.3862940.0000000.6931470.0000002013-11-10 16:05:372023-08-31 14:26:0820.8697420.0000000.5265890.000000
19762HumanTrueFalseFalseFalseFalseFalseNaN0.0000000.0000000.0000000.0000002020-10-01 18:30:322020-12-29 19:45:1200.0000000.0000000.0000000.000000
19763BotTrueFalseTrueTrueTrueFalseTony came to Linux in 1994 and has never looked back. His entire professional career has been spent working with or on Linux. First as a systems administrator3.6109182.8332132.4849071.6094382014-07-02 23:27:342023-08-15 16:38:3401.5284271.3437031.2484410.959135
19764HumanTrueFalseFalseFalseFalseFalseNaN2.8332130.0000001.3862940.0000002017-12-06 21:56:312023-07-26 18:32:2501.3437030.0000000.8697420.000000
19765HumanTrueFalseTrueFalseTrueFalseSoftware engineer at RealTracs.2.6390570.0000002.3978950.6931472015-11-14 14:44:052022-08-23 21:09:4901.2917250.0000001.2231560.526589
19766HumanTrueFalseTrueFalseFalseFalseNaN2.0794420.0000001.0986120.0000002021-11-23 18:55:292023-10-06 22:50:4501.1247480.0000000.7412760.000000
19767BotTrueFalseFalseFalseTrueFalseNaN2.3978950.0000000.6931470.0000002016-04-22 22:11:592022-07-07 19:48:2101.2231560.0000000.5265890.000000